Absolute Risk Estimates Research Articles

Individual and community-level risk for COVID-19 mortality in the United States.

Reducing COVID-19 burden for populations will require equitable and effective risk-based allocations of scarce preventive resources, including vaccinations1. To aid in this effort, we developed a general population risk calculator for COVID-19 mortality based on various sociodemographic factors and pre-existing conditions for the US population, combining information from the UK-based OpenSAFELY study with mortality rates by age and ethnicity across US states. We tailored the tool to produce absolute risk estimates in future time frames by incorporating information on pandemic dynamics at the community level. We applied the model to data on risk factor distribution from a variety of sources to project risk for the general adult population across 477 US cities and for the Medicare population aged 65 years and older across 3,113 US counties, respectively. Validation analyses using 54,444 deaths from 7 June to 1 October 2020 show that the model is well calibrated for the US population. Projections show that the model can identify relatively small fractions of the population (for example 4.3%) that might experience a disproportionately large number of deaths (for example 48.7%), but there is wide variation in risk across communities. We provide a web-based risk calculator and interactive maps for viewing community-level risks.

PND72 Measuring the IMPACT of Almitrine Marketing Authorisation Withdrawal on Public Health in France

Impact of pharmacovigilance (PV) actions such as marketing authorisation (MA) withdrawal on public health is not normally measured quantitatively. Removing medicines from market may reduce adverse drug reactions (ADRs) significantly affecting public health (PH), which should be measured quantitatively if possible. This study identified a method to quantify impact of MA withdrawal in terms of morbidity and mortality changes. Almitrine's MA withdrawal throughout France (2013) due to cases of peripheral nervous system (PNS) disturbance and severe weight loss, demonstrates the model. Patients exposed to almitrine were counted in quarterly periods for 12 months pre- and post-MA withdrawal, in IQVIA Medical Research Data (IMRD)-France electronic health record database; annual exposure was estimated. Reason for MA withdrawal (ADR of interest) was obtained from EMA records. Quantitative research addressing the ADR of interest was identified by systematic review, allowing an estimation of absolute risk in exposed patients. PH impact of PV action, expressed as crude annual prevention of ADRs, was estimated by modelling product usage figures and ADR risk. At the time of almitrine's withdrawal, 7,456,368 patients were active in IMRD-France. Prevalence of almitrine exposure during the 12 months before withdrawal was 0.82 per 100,000 IMRD-France patients. Exposure to almitrine decreased by 51.9% in the population after its withdrawal, but some exposure remained after 12 months. Risk of PNS disturbance was 15.5% and severe weight loss was 7.8%, estimated from published studies. Based on risk and usage, withdrawal of almitrine from market could prevent up to 13 cases of PNS disturbance and 3 cases of severe weight loss annually amongst IMRD-France patients if exposure were eliminated. This model quantifies PH impact of almitrine’s MA withdrawal in terms of serious morbidity, assuming exposure was eliminated. This method can be applied to other products in a range of settings.

Accuracy of signs, symptoms and blood tests for diagnosing acute bacterial rhinosinusitis and CT-confirmed acute rhinosinusitis in adults: protocol of an individual patient data meta-analysis

IntroductionThis protocol outlines a diagnostic individual patient data (IPD) meta-analysis aimed at developing simple prediction models based on readily available signs, symptoms and blood tests to accurately predict acute bacterial...

Mammary Chain Irradiation in Left-Sided Breast Cancer: Can We Reduce the Risk of Secondary Cancer and Ischaemic Heart Disease with Modern Intensity-Modulated Radiotherapy Techniques?

Introduction: The aim of the present study was to estimate the impact of the addition of internal mammary chain (IMC) irradiation in node-positive left-sided breast cancer (BC) patients undergoing regional nodal irradiation (RNI) and comparatively evaluate excess relative and absolute risks of radiation-induced lung cancer/BC and ischaemic heart disease for intensity-modulated radiotherapy (IMRT) versus 3D conformal radiotherapy (3D-CRT). Methods: Four treatment plans were created (3D-CRT and IMRT –/+ IMC) for each of the 10 evaluated patients, and estimates of excess relative risk (ERR) and 10-year excess absolute risk (EAR) were calculated for radiation-induced lung cancer/BC and coronary events using linear, linear-exponential and plateau models. Results: The addition of IMC irradiation to RNI significantly increased the dose exposure of the heart, lung and contralateral breast using both techniques, increasing ERR for secondary lung cancer (58 vs. 44%, p = 0.002), contralateral BC (49 vs. 31%, p = 0.002) and ischaemic heart disease (41 vs. 27%, p = 0.002, IMRT plans). IMRT significantly reduced the mean cardiac dose and mean lung dose as compared to 3D-CRT, decreasing ERR for major coronary events (64% 3D-CRT vs. 41% IMRT, p = 0.002) and ERR for secondary lung cancer (75 vs. 58%, p = 0.004) in IMC irradiation, without a significant impact on secondary contralateral BC risks. Conclusion: Although IMC irradiation has been shown to increase survival rates in node-positive BC patients, it increased dose exposure of organs at risk in left-sided BC, resulting in significantly increased risks for secondary lung cancer/contralateral BC and ischaemic heart disease. In this setting, the adoption of IMRT seems advantageous when compared to 3D-CRT.

Optimizing Atherosclerotic Cardiovascular Disease Risk Estimation for Veterans With Diabetes Mellitus.

Estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk in diabetes mellitus patients is used to guide primary prevention, but the performance of risk estimators (2013 Pooled Cohort Equations [PCE] and Risk Equations for Complications of Diabetes [RECODe]) varies across populations. Data from electronic health records could be used to improve risk estimation for a health system's patients. We aimed to evaluate risk equations for initial ASCVD events in US veterans with diabetes mellitus and improve model performance in this population. We studied 183 096 adults with diabetes mellitus and without prior ASCVD who received care in the Veterans Affairs Healthcare System (VA) from 2002 to 2016 with mean follow-up of 4.6 years. We evaluated model discrimination, using Harrell's C statistic, and calibration, using the reclassification χ2 test, of the PCE and RECODe equations to predict fatal or nonfatal myocardial infarction or stroke and cardiovascular mortality. We then tested whether model performance was affected by deriving VA-specific β-coefficients. Discrimination of ASCVD events by the PCE was improved by deriving VA-specific β-coefficients (C statistic increased from 0.560 to 0.597) and improved further by including measures of glycemia, renal function, and diabetes mellitus treatment (C statistic, 0.632). Discrimination by the RECODe equations was improved by substituting VA-specific coefficients (C statistic increased from 0.604 to 0.621). Absolute risk estimation by PCE and RECODe equations also improved with VA-specific coefficients; the calibration P increased from <0.001 to 0.08 for PCE and from <0.001 to 0.005 for RECODe, where higher P indicates better calibration. Approximately two-thirds of veterans would meet a guideline indication for high-intensity statin therapy based on the PCE versus only 10% to 15% using VA-fitted models. Existing ASCVD risk equations overestimate risk in veterans with diabetes mellitus, potentially impacting guideline-indicated statin therapy. Prediction model performance can be improved for a health system's patients using readily available electronic health record data.

Artificial Intelligence-related Literature in Transplantation: A Practical Guide.

Artificial Intelligence-related Literature in Transplantation: A Practical Guide.

Abstract 2321: Validating breast cancer risk prediction models in the Korean Cancer Prevention Study-II Biobank

Abstract Background: Informative and well-calibrated risk prediction models may be useful for precision breast-cancer screening. However, these models often have not been validated across diverse populations. Validation in East Asian populations is particularly lacking. Objective: To evaluate the performance of breast cancer risk models developed in European-ancestry studies in a Korean population, both directly and after recalibration. Methods: We compared discrimination and calibration of three multivariable risk models in a cohort of 77,457 women from the Korean Cancer Prevention Study-II. The first incorporated US breast-cancer incidence and mortality rates, US risk-factor distributions, and relative risk (RR) estimates from European-ancestry studies. The second recalibrated the first by using Korean incidence and mortality rates and Korean risk factor distributions, while retaining the European-ancestry RR estimates. Finally, we derived a Korea-specific model incorporating the RR estimates from Korean Cancer Prevention Study. The Individualized Coherent Absolute Risk Estimation (iCARE) software package was used to calculate 5 year predicted absolute risks of breast cancer and assess model performance. Results: The US European-ancestry breast cancer risk model was well calibrated among Korean women &amp;lt;50 years (E/O=1.124 (0.989, 1.278)) but markedly overestimated the risk for those ≥50 years (E/O=2.472 (2.005, 3.049)). Recalibrating absolute risk estimates using Korean breast cancer rates and risk distributions markedly improved the calibration in women ≥50 (E/O=1.018 (0.825, 1.255)). The model incorporating Korean-based RRs had similar but not clearly improved performance relative to the recalibrated model; this may reflect at most modest differences in the RRs between European and Korean populations and the relatively small sample used to estimate RRs among Korean women. Conclusions: The poor performance of the US European-ancestry breast cancer risk model among older Korean women highlights the importance of tailoring absolute risk models to specific populations. Recalibrating the model using Korean incidence and mortality rates and risk factor distributions greatly improved performance; we anticipate the performance could be further improved by incorporating multivariable risk-factor RRs from large Korea-based studies. Citation Format: Yon Ho Jee, Chi Gao, Jihye Kim, Seho Park, Sun Ha Jee, Peter Kraft. Validating breast cancer risk prediction models in the Korean Cancer Prevention Study-II Biobank [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2321.

Cancer risk after breast proton therapy considering physiological and radiobiological uncertainties

BackgroundThe reduced normal tissue dose burden from protons can reduce the risk of second cancer for breast cancer patients. Breathing motion and the impact of variable relative biological effectiveness (RBE) are however concerns for proton dose distributions. This study aimed to quantify the impact of these factors on risk predictions from proton and photon therapy. Materials and methodsTwelve patients were planned in free breathing with protons and photons to deliver 50 Gy (RBE) in 25 fractions (assuming RBE = 1.1 for protons) to the left breast. Second cancer risk was evaluated with several models for the lungs, contralateral breast, heart and esophagus as organs at risk (OARs). Plans were recalculated on CT-datasets acquired in extreme phases to account for breathing motion. Proton plans were also recalculated assuming variable RBE for a range of radiobiological parameters. ResultsThe OARs received substantially lower doses from protons compared to photons. The highest risks were for the lungs (average second cancer risks of 0.31% and 0.12% from photon and proton plans, respectively). The reduced risk with protons was maintained, even when breathing and/or RBE variation were taken into account. Furthermore, while the total risks from the photon plans were seen to increase with the integral dose, no such correlation was observed for the proton plans. ConclusionsProtons have an advantage over the photons with respect to the induction of cancer. Uncertainties in physiological movements and radiobiological parameters affected the absolute risk estimates, but not the general trend of lower risk associated with proton therapy.

Volumetric modulated arc therapy versus intensity-modulated proton therapy in the postoperative irradiation of thymoma.

To investigate the role of intensity-modulated proton therapy (IMPT) compared to volumetric modulated arc therapy (VMAT) for the radiation treatment of thymoma cancer. Twenty patients were retrospectively planned for IMPT [with (IMPT_R1 or IMPT_R2 according to the approach adopted) and without robust optimization] and VMAT. The results were compared according to dose-volume metrics on the clinical and planning target volumes (CTV and PTV) and the main organs at risk (heart, breasts, lungs, spinal cord and oesophagus). Estimates of the excess absolute risk (EAR) of secondary cancer induction were determined for the oesophagus, the breasts and the composite lungs. For the heart, the relative risk (RR) of chronic heart failure (CHF) was assessed. IMPT and VMAT plans resulted equivalent in terms of target coverage for both the CTV and the PTV. The CTV homogeneity index resulted in 0.03 ± 0.01 and 0.04 ± 0.01 for VMAT and all IMPT plans, respectively. The conformality index resulted in 1.1 ± 0.1 and 1.2 ± 0.1 for VMAT and all IMPT plans. The mean dose to the breasts resulted in 10.5 ± 5.0, 4.5 ± 3.4, 4.7 ± 3.5 and 4.6 ± 3.4Gy for VMAT, IMPT, IMPT_R1 and IMPT_R2. For the lungs, the mean dose was 9.6 ± 2.3, 3.5 ± 1.5, 3.6 ± 1.6 and 3.8 ± 1.4Gy; for the heart: 8.7 ± 4.4, 4.3 ± 1.9, 4.5 ± 2.0 and 4.4 ± 2.4Gy and for the oesophagus 8.2 ± 3.5, 2.2 ± 3.4, 2.4 ± 3.6 and 2.5 ± 3.5Gy. The RR for CHF was 1.6 ± 0.3 for VMAT and 1.3 ± 0.2 for IMPT (R1 or R2). The EAR was 3.6 ± 0.v vs 1.0 ± 0.6 or 1.2 ± 0.6 (excess cases/10,000 patients year) for the oesophagus; 17.4 ± 6.5 vs 5.7 ± 3.2 or 6.1 ± 3.8 for the breasts and 24.8 ± 4.3 vs 8.1 ± 2.7 or 8.7 ± 2.3 for the composite lungs for VMAT and IMPT_R, respectively. The data from this in-silico study suggest that intensity-modulated proton therapy could be significantly advantageous in the treatment of thymoma patients with particular emphasis to a substantial reduction of the risk of cardiac failure and secondary cancer induction. Robust planning is a technical pre-requisite for the safety of the delivery.

Validating Breast Cancer Risk Prediction Models in the Korean Cancer Prevention Study-II Biobank.

Risk prediction models may be useful for precision breast cancer screening. We aimed to evaluate the performance of breast cancer risk models developed in European-ancestry studies in a Korean population. We compared discrimination and calibration of three multivariable risk models in a cohort of 77,457 women from the Korean Cancer Prevention Study (KCPS)-II. The first incorporated U.S. breast cancer incidence and mortality rates, U.S. risk factor distributions, and RR estimates from European-ancestry studies. The second recalibrated the first by using Korean incidence and mortality rates and Korean risk factor distributions, while retaining the European-ancestry RR estimates. Finally, we derived a Korea-specific model incorporating the RR estimates from KCPS. The U.S. European-ancestry breast cancer risk model was well calibrated among Korean women <50 years [expected/observed = 1.124 (0.989, 1.278)] but markedly overestimated the risk for those ≥50 years [E/O = 2.472 (2.005, 3.049)]. Recalibrating absolute risk estimates using Korean breast cancer rates and risk distributions markedly improved the calibration in women ≥50 [E/O = 1.018 (0.825, 1.255)]. The model incorporating Korean-based RRs had similar but not clearly improved performance relative to the recalibrated model. The poor performance of the U.S. European-ancestry breast cancer risk model among older Korean women highlights the importance of tailoring absolute risk models to specific populations. Recalibrating the model using Korean incidence and mortality rates and risk factor distributions greatly improved performance. The data will provide valuable information to plan and evaluate actions against breast cancer focused on primary prevention and early detection in Korean women.

Volumetric modulated arc therapy versus intensity-modulated proton therapy in neoadjuvant irradiation of locally advanced oesophageal cancer

BackgroundTo investigate the role of intensity-modulated proton therapy (IMPT) compared to volumetric modulated arc therapy (VMAT), realised with RapidArc and RapidPlan methods (RA_RP) for neoadjuvant radiotherapy in locally advanced oesophagal cancer.MethodsTwenty patients were retrospectively planned for IMPT (with two fields, (IMPT_2F) or with three fields (IMPT_3F)) and RA_RP and the results were compared according to dose-volume metrics. Estimates of the excess absolute risk (EAR) of secondary cancer induction were determined for the lungs. For the cardiac structures, the relative risk (RR) of coronary artery disease (CAD) and chronic heart failure (CHF) were estimated.ResultsBoth the RA_RP and IMPT approached allowed to achieve the required coverage for the gross tumour volume, (GTV) and the clinical and the planning target volumes, CTV and PTV (V98% > 98 for CTV and GTV and V95% > 95 for the PTV)). The conformity index resulted in 0.88 ± 0.01, 0.89 ± 0.02 and 0.89 ± 0.02 for RA_RP, IMPT_2F and IMPT_3F respectively. With the same order, the homogeneity index for the PTV resulted in 5.6 ± 0.6%, 4.4 ± 0.9% and 4.5 ± 0.8%. Concerning the organs at risk, the IMPT plans showed a systematic and statistically significant incremental sparing when compared to RA_RP, especially for the heart. The mean dose to the combined lungs was 8.6 ± 2.9 Gy for RA_RP, 3.2 ± 1.5 Gy and 2.9 ± 1.2 Gy for IMPT_2F and IMPT_3F. The mean dose to the whole heart resulted to 9.9 ± 1.9 Gy for RA_RP compared to 3.7 ± 1.3 Gy or 4.0 ± 1.4 Gy for IMPT_2F or IMPT_3F; the mean dose to the left ventricle resulted to 6.5 ± 1.6 Gy, 1.9 ± 1.5 Gy, 1.9 ± 1.6 Gy respectively. Similar sparing effects were observed for the liver, the kidneys, the stomach, the spleen and the bowels.The EAR per 10,000 patients-years of secondary cancer induction resulted in 19.2 ± 5.7 for RA_RP and 6.1 ± 2.7 for IMPT_2F or 5.7 ± 2.4 for IMPT_3F. The RR for the left ventricle resulted in 1.5 ± 0.1 for RA_RP and 1.1 ± 0.1 for both IMPT sets. For the coronaries, the RR resulted in 1.6 ± 0.4 for RA_RP and 1.2 ± 0.3 for protons.ConclusionWith regard to cancer of the oesophagogastric junction type I and II, the use of intensity-modulated proton therapy seems to have a clear advantage over VMAT. In particular, the reduction of the heart and abdominal structures dose could result in an optimised side effect profile. Furthermore, reduced risk of secondary neoplasia in the lung can be expected in long-term survivors and would be a great gain for cured patients.

O7.3. EXPLORING COMORBIDITY BETWEEN MENTAL DISORDERS AND GENERAL MEDICAL CONDITIONS

BackgroundA large body of evidence shows that comorbidity is pervasive among individuals being treated for mental disorders, including schizophrenia. Those with mental disorders have been found to have higher rates of comorbidity and associated mortality due to a range of general medical conditions. However much of the evidence relies on surveys, cross-sectional studies and retrospectively collected data.MethodsUsing the Danish registers, we identified those born in Denmark (1900–2015) and who resided there during the study period (2000–2016). Information was obtained on ten groups of mental disorders (MDs), including “Schizophrenia spectrum disorders” (corresponding to ICD-10 F20-F29 codes). We compared the rate of diagnosis with nine general medical conditions (GMC) categories between those exposed and unexposed to each MD. Overall and lagged hazard ratios, with 95% confidence intervals, were calculated using Cox proportional hazards models. Absolute risks were estimated by competing risks survival analyses.ResultsThe cohort followed 5.9 million individuals for 83.9 million person years. For the majority of prior MD-later GMC pairs, receiving a MD diagnosis increased the risk of subsequent GMC diagnosis; however, there were some exceptions. Focusing on those diagnosed with schizophrenia, elevated rates of diagnosis were observed for circulatory, endocrine, pulmonary, gastrointestinal, urogenital and hematological GMCs, and cancers. Various patterns were seen for lagged HRs. Results for schizophrenia generally showed hazard ratios for GMC diagnoses were most elevated the first couple of years after schizophrenia diagnosis. Cumulative incidence proportions for each GMC category 15 years after diagnosis with schizophrenia varied between 3.4% (for urogenital GMCs, 95% CI 3.2, 3.6) and 34.0% (for circulatory GMCs, 95% CI 33.51, 34.57).DiscussionThis study provides a comprehensive picture of GMC comorbidity among those with diagnosed with MDs in Denmark. It is the first study, to our knowledge, to provide both relative and absolute measures of the risk of such comorbidity. We hope that our findings related to specific MD-GMC comorbidity pairs will stimulate future research to explore mechanisms of action underlying of the observed associations. The provision of absolute risk estimates may aid in identifying those at greater need of primary prevention of GMC comorbidity. As MDs typically have a younger age of onset than many GMCs, natural critical windows exist for the reduction of secondary comorbidity.

Sample-weighted semiparametric estimation of cause-specific cumulative risk and incidence using left- or interval-censored data from electronic health records.

Electronic health records (EHRs) can be a cost-effective data source for forming cohorts and developing risk models in the context of disease screening. However, important issues need to be handled: competing outcomes, left-censoring of prevalent disease, interval-censoring of incident disease, and uncertainty of prevalent disease when accurate disease ascertainment is not conducted at baseline. Furthermore, novel tests that are costly and limited in availability can be conducted on stored biospecimens selected as samples from EHRs by using different sampling fractions. We extend sample-weighted semiparametric marginal mixture models to estimating competing risks. For flexible modeling of relative risks, a general transformation of the subdistribution hazard function and regression parameters is used. We propose a numerical algorithm for nonparametrically calculating the maximum likelihood estimates for subdistribution hazard functions and regression parameters. Methods for calculating the consistent confidence intervals for relative and absolute risk estimates are presented. The proposed algorithm and methods show reliable finite sample performance through simulation studies. We apply our methods to a cohort assembled from EHRs at a health maintenance organization where we estimate cumulative risk of cervical precancer/cancer and incidence of infection-clearance by HPV genotype among human papillomavirus (HPV) positive women. There is no significant difference in 3-year HPV-clearance rates across different HPV types, but 3-year cumulative risk of progression-to-precancer/cancer from HPV-16 is relatively higher than the other HPV genotypes.

Prediction of cardiovascular events in rheumatoid arthritis using risk age calculations: evaluation of concordance across risk age models

BackgroundIn younger individuals, low absolute risk of cardiovascular disease (CVD) may conceal an increased risk age and relative risk of CVD. Calculation of risk age is proposed as an adjuvant to absolute CVD risk estimation in European guidelines. We aimed to compare the discriminative ability of available risk age models in prediction of CVD in rheumatoid arthritis (RA). Secondly, we also evaluated the performance of risk age models in subgroups based on RA disease characteristics.MethodsRA patients aged 30–70 years were included from an international consortium named A Trans-Atlantic Cardiovascular Consortium for Rheumatoid Arthritis (ATACC-RA). Prior CVD and diabetes mellitus were exclusion criteria. The discriminatory ability of specific risk age models was evaluated using c-statistics and their standard errors after calculating time until fatal or non-fatal CVD or last follow-up.ResultsA total of 1974 patients were included in the main analyses, and 144 events were observed during follow-up, the median follow-up being 5.0 years. The risk age models gave highly correlated results, demonstrating R2 values ranging from 0.87 to 0.97. However, risk age estimations differed > 5 years in 15–32% of patients. C-statistics ranged 0.68–0.72 with standard errors of approximately 0.03. Despite certain RA characteristics being associated with low c-indices, standard errors were high. Restricting analysis to European RA patients yielded similar results.ConclusionsThe cardiovascular risk age and vascular age models have comparable performance in predicting CVD in RA patients. The influence of RA disease characteristics on the predictive ability of these prediction models remains inconclusive.

Estimation of Absolute Risk of Colorectal Cancer Based on Healthy Lifestyle, Genetic Risk, and Colonoscopy Status in a Population-Based Study

Estimates of absolute risk of colorectal cancer (CRC) are needed to facilitate communication and better inform the public about the potentials and limits of cancer prevention. Using data from a large population-based case-control study in Germany (Darmkrebs: Chancen der Verhütung durch Screening [DACHS] study, which began in 2003) and population registry data, we calculated 30-year absolute risk estimates for development of CRC based on a healthy lifestyle score (derived from 5 modifiable lifestyle factors: smoking, alcohol consumption, diet, physical activity, and body fatness), a polygenic risk score (based on 90 single-nucleotide polymorphisms), and colonoscopy history. We analyzed data from 4220 patients with CRC and 3338 individuals without CRC. Adherence to a healthy lifestyle and colonoscopy in the preceding 10 years were associated with a reduced relative risk of CRC in men and women. We observed a higher CRC risk in participants with high or intermediate genetic risk scores. For 50-year-old men and women without a colonoscopy, the absolute risk of CRC varied according to the polygenic risk score and the healthy lifestyle score (men, 3.5%-13.4%; women, 2.5%-10.6%). For 50-year-old men and women with a colonoscopy, the absolute risk of developing CRC was much lower but still varied according to the polygenic risk score and the healthy lifestyle score (men, 1.2%-4.8%; women, 0.9%-4.2%). Among all risk factor profiles, the 30-year absolute risk estimates consistently decreased with adherence to a healthy lifestyle. In a population-based study, we found that a colonoscopy can drastically reduce the absolute risk of CRC and that the genetically predetermined risk of CRC can be further reduced by adherence to a healthy lifestyle. Our results show the magnitude of CRC prevention possible through colonoscopy and lifestyle at a predefined genetic risk. This observational study has been registered in the German Clinical Trials Register (DRKS00011793), which is a primary registry in the World Health Organization Registry Network.

GPs' understanding of the benefits and harms of treatments for long-term conditions: an online survey.

BackgroundGPs prescribe multiple long-term treatments to their patients. For shared clinical decision-making, understanding of the absolute benefits and harms of individual treatments is needed. International evidence shows that doctors’ knowledge of treatment effects is poor but, to the authors knowledge, this has not been researched among GPs in the UK.AimTo measure the level and range of the quantitative understanding of the benefits and harms of treatments for common long-term conditions (LTCs) among GPs.Design & settingAn online cross-sectional survey was distributed to GPs in the UK.MethodParticipants were asked to estimate the percentage absolute risk reduction or increase conferred by 13 interventions across 10 LTCs on 17 important outcomes. Responses were collated and presented in a novel graphic format to allow detailed visualisation of the findings. Descriptive statistical analysis was performed.ResultsA total of 443 responders were included in the analysis. Most demonstrated poor (and in some cases very poor) knowledge of the absolute benefits and harms of treatments. Overall, an average of 10.9% of responses were correct allowing for ±1% margin in absolute risk estimates and 23.3% allowing a ±3% margin. Eighty-seven point seven per cent of responses overestimated and 8.9% of responses underestimated treatment effects. There was no tendency to differentially overestimate benefits and underestimate harms. Sixty-four point eight per cent of GPs self-reported ‘low’ to ‘very low’ confidence in their knowledge.ConclusionGPs’ knowledge of the absolute benefits and harms of treatments is poor, with inaccuracies of a magnitude likely to meaningfully affect clinical decision-making and impede conversations with patients regarding treatment choices.

Prediction of pre-eclampsia in nulliparous women using routinely collected maternal characteristics: a model development and validation study

BackgroundGuidelines recommend identifying in early pregnancy women at elevated risk of pre-eclampsia. The aim of this study was to develop and validate a pre-eclampsia risk prediction model for nulliparous women attending routine antenatal care “the Western Sydney (WS) model”; and to compare its performance with the National Institute of Health and Care Excellence (NICE) risk factor-list approach for classifying women as high-risk.MethodsThis retrospective cohort study included all nulliparous women who gave birth in three public hospitals in the Western-Sydney-Local-Health-District, Australia 2011–2014. Using births from 2011 to 2012, multivariable logistic regression incorporated established maternal risk factors to develop and internally validate the WS model. The WS model was then externally validated using births from 2013 to 2014, assessing its discrimination and calibration. We fitted the final WS model for all births from 2011 to 2014, and compared its accuracy in predicting pre-eclampsia with the NICE approach.ResultsAmong 12,395 births to nulliparous women in 2011–2014, there were 293 (2.4%) pre-eclampsia events. The WS model included: maternal age, body mass index, ethnicity, multiple pregnancy, family history of pre-eclampsia, autoimmune disease, chronic hypertension and chronic renal disease. In the validation sample (6201 births), the model c-statistic was 0.70 (95% confidence interval 0.65–0.75). The observed:expected ratio for pre-eclampsia was 0.91, with a Hosmer-Lemeshow goodness-of-fit test p-value of 0.20. In the entire study sample of 12,395 births, 374 (3.0%) women had a WS model-estimated pre-eclampsia risk ≥8%, the pre-specified risk-threshold for considering aspirin prophylaxis. Of these, 54 (14.4%) developed pre-eclampsia (sensitivity 18% (14–23), specificity 97% (97–98)). Using the NICE approach, 1173 (9.5%) women were classified as high-risk, of which 107 (9.1%) developed pre-eclampsia (sensitivity 37% (31–42), specificity 91% (91–92)). The final model showed similar accuracy to the NICE approach when using lower risk-threshold of ≥4% to classify women as high-risk for pre-eclampsia.ConclusionThe WS risk model that combines readily-available maternal characteristics achieved modest performance for prediction of pre-eclampsia in nulliparous women. The model did not outperform the NICE approach, but has the advantage of providing individualised absolute risk estimates, to assist with counselling, inform decisions for further testing, and consideration of aspirin prophylaxis.

‘Don’t know’ responding and estimates of perceived risk: failing to provide a ‘don’t know’ response systematically biases laypeople’s perceived risk estimates

Individuals sometimes report that they don’t know their risk for a health problem, but many studies of illness risk perception do not provide a ‘don’t know’ (DK) response option. We examined how providing versus not providing a DK option affected responding – either the number of DK responses when the option was provided or the number of skips when a DK option was not provided. We also examined whether it systematically affected perceived risk estimates. Participants (N = 960) reported perceived absolute and comparative risk for four health issues. They were randomly assigned to receive risk questions with or without a DK response option. In the DK Option condition, ‘don’t know’ answers ranged from 3.5% to 12.7% of responses. For all absolute risk questions, perceived risk was significantly higher in the DK Option condition; all Fs (1, 932) < 3.79, all ps < .05. DK responding was issue-specific; more than 25% of the sample answered DK to at least once, but only 2.7% answered DK to more than half of the items. Substantial use of the DK response option and higher perceived absolute risk estimates were seen when a DK response was provided. These findings have implications for assessing the validity of measured risk estimates, of relations between constructs, and for survey design best practices. In addition, they suggest that DK responses are primarily knowledge-based – that individuals answer DK largely because of lacking sufficient knowledge or health literacy to form a risk perception rather than due to satisficing.

841: Late preterm antenatal corticosteroids and neonatal morbidity and mortality: estimating population-level absolute risk measures

Randomized trial evidence suggests administration of antenatal corticosteroids (ACS) in the late preterm period (34-36 weeks) reduces neonatal respiratory complications. However, the risk-benefit tradeoff is less clear than in earlier gestational ages because baseline risks of adverse outcomes are lower, which may increase the number needed to treat (NNT) and decrease absolute risk reductions (ARRs) from week to week. Estimates of ARRs and NNTs based on population baseline rates of neonatal morbidity and mortality in the US population are critical for clinical counseling but are not readily available. Our objective was to generate these absolute risk estimates associated with ACS administration in late preterm gestations. We conducted a retrospective cohort study of births at 34-36 weeks’ gestation in the United States in 2012 and 2013 using the US Linked Birth-Infant Death records (CDC). Neonates were excluded if their mothers received ACS. Outcomes were neonatal death, NICU admission, assisted ventilation, and surfactant use. We applied relative risk estimates from recent randomized controlled trials to baseline risks in the US population to generate ARRs and NNTs with 95% confidence intervals for neonatal morbidity associated with ACS administration, stratified by gestational week at birth. There were 543,259 late preterm births in the United States in 2012-2013. ARRs for neonatal mortality and morbidities decreased with advancing gestational age, and NNTs increased (Figures 1 and 2). At 34 weeks’ gestation, 31 (95% CI, 20-85) pregnant women would need to be treated with ACS to prevent one assisted ventilation; at 36 weeks, the number was 83 (54-229). Likewise, the NNT for surfactant use was 123 (77-4796) at 34 weeks and 518 (326-20212) at 36 weeks. Our estimates of absolute risk measures derived from population baseline risks of neonatal morbidities are generalizable to the general obstetrical population. These absolute risk measures may be more useful for patient counseling and clinical decision making than relative risks from randomized trial populations alone.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Knee Fracture Increases TKA Risk After Initial Fracture Treatment and Throughout Life.

Knee fractures may lead to post-traumatic knee osteoarthritis and subsequent TKA in some patients. However, absolute risk estimates and risk factors for TKA in patients with knee fractures compared with those of the general population remain largely unknown. Such knowledge would help establish the treatment burden and direct patient counseling after a knee fracture is sustained. (1) What is the short-term risk of TKA after knee fracture? (2) What is the long-term risk of TKA after knee fracture? (3) What are the risk factors for TKA in patients with knee fractures? A nationwide 20-year, matched-case comparison cohort study of prospectively collected data from the Danish National Patient Registry included all patients at least 15 years old with International Classification of Diseases, 10th revision codes DS724, DS820, or DS821 (knee fractures) on the date their knee fracture was registered. Each patient with a knee fracture was matched (by sex and age) to five people without knee fractures from the general Danish population on the date the knee fracture patient's knee fracture was registered (population controls). Patients with knee fractures and people in the population control group were followed from the date the knee fracture patient's knee fracture was registered to the date of TKA, amputation, knee fusion, emigration, death, or end of follow-up in April 2018. TKA risks for patients with knee fractures versus those for population controls and TKA risk factors in patients with knee fractures were estimated using hazard ratios (HRs) with 95% CIs. A total of 48,791 patients with knee fractures (median age 58 years [interquartile range 41-73]; 58% were female) were matched to 263,593 people in the population control group. The HR for TKA in patients with knee fractures compared with population controls was 3.74 (95% CI 3.44 to 4.07; p < 0.01) in the first 3 years after knee fracture. Among knee fracture patients, the risk of undergoing TKA was 2% (967 of 48,791) compared with 0.5% (1280 of 263,593) of people in the population control group. After the first 3 years, the HR was 1.59 (95% CI 1.46 to 1.71) and the number of patients with knee fractures with TKA events divided by the number at risk was 2% (849 of 36,272), compared with 1% (2395 of 180,418) of population controls. During the 20-year study period, 4% of patients with knee fractures underwent TKA compared with 1% of population controls. Risk factors for TKA in patients with knee fractures were: primary knee osteoarthritis (OA) versus no primary knee OA (HR 9.57 [95% CI 5.39 to 16.98]), surgical treatment with external fixation versus open reduction and internal fixation and reduction only (HR 1.92 [95 % CI 1.01 to 3.66]), proximal tibia fracture versus patellar fracture (HR 1.75 [95 % CI 1.30 to 2.36]), and distal femur fracture versus patellar fracture (HR 1.68 [95 % CI 1.08 to 2.64]). Surgical treatment of knee fractures was also a risk factor for TKA. The HRs for TKA in patients with knee fractures who were surgically treated versus those who were treated non-surgically were 2.05 (95% CI 1.83 to 2.30) in the first 5 years after knee fracture and 1.19 (95% CI 1.01 to 1.41) after 5 years. Patients with knee fractures have a 3.7 times greater risk of TKA in the first 3 years after knee fracture, and the risk remains 1.6 times greater after 3 years and throughout their lifetimes. Primary knee OA, surgical treatment of knee fractures, external fixation, proximal tibia fractures, and distal femur fractures are TKA risk factors. These risk estimates and risk factors highlight the treatment burden of knee fractures, building a foundation for future studies to further counsel patients on their risk of undergoing TKA based on patient-, fracture-, and treatment-specific factors. Level III, prognostic study.